Adaptive linear predictive Kalman filter compressed sensing algorithm

被引：0
|
作者：
Tian J.-P. [1 ,2 ]
Min T. [1 ]
Xue Y. [1 ]
Zheng G.-X. [1 ,2 ]
机构：
[1] School of Communication and Information Engineering, Shanghai University, Shanghai
[2] Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai
来源：
Kongzhi yu Juece/Control and Decision | 2020年 / 35卷 / 01期
关键词：
Compressed sensing; Kalman filtering; Linear prediction; Reconstruction algorithm; Signal reconstruction; Time-varying sparse signals;
D O I：
10.13195/j.kzyjc.2018.0679
中图分类号：
学科分类号：
摘要：
A Kalman filter algorithm based on adaptive linear predictive is proposed for the reconstruction of time-varying sparse signals in compressed sensing. The signal is observed from a sliding window. Based on the correlations between the signals of two continuous windows and the adaptive linear prediction, the state transfer equation of continuous windows signal is obtained. The equation and the modified observation equation constitute the system state-space model. In the signal reconstruction stage, a greedy algorithm is employed to determine the support set and reduced order Kalman filter iteration to get the exact solution. This paper simulates the FM, AM, WiFi RF and voice sampling signals. The simulation results show that the proposed algorithm recovering performance is improved without much increase in complexity. The reconstruction accuracy is improved about 5 percent as compared with that using the cycle spinning model, and far higher than other similar algorithms. Meanwhile, the SNR of reconstructed signal is about 1~8dB higher than that of original signal in the different noise environment, which shows that the algorithm has strong anti noise performance. © 2020, Editorial Office of Control and Decision. All right reserved.
引用
收藏
页码：83 / 90
页数：7
相关论文
共 20 条
  • [1] Donoho D.L., Compressed sensing, IEEE Transactions on Information Theory, 52, 4, pp. 1289-1306, (2006)
  • [2] Candes E.J., Compressive sampling, Proceedings of the 2006 International Congress of Mathematicians, pp. 1433-1452, (2006)
  • [3] Candes E.J., Romberg J., Tao T., Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information, IEEE Transaction Info Theory, 52, 2, pp. 489-509, (2006)
  • [4] Jing N., Bi W.H., Hu Z.P., Et al., A survey on dynamic compressed sensing, Acta Automatica Sinica, 41, 1, pp. 22-37, (2015)
  • [5] Meenakshi, Budhiraja S., Asurvey of compressive sensing based greedy pursuit reconstruction algorithms, International Journal of Image, Graphics and Signal Processing, 7, 10, pp. 1-10, (2015)
  • [6] Akhila A.T., Divya R., A survey on greedy reconstruction algorithms in compressive sensing, International Journal of Research in Computer and Communication Technology, 3, 5, pp. 126-129, (2016)
  • [7] Qaisar S., Bilal R., Iqbal M., Et al., Compressive sensing: From theory to applications a survey, Journal of Communications and Networks, 15, 5, pp. 443-456, (2013)
  • [8] Fang H., Yang H.R., Greedy algorithms and compressed sensing, Acta Automatica Sinica, 37, 12, pp. 1413-1421, (2011)
  • [9] Tian J.P., Liu X.J., Zheng G.X., Avariable step size sparsity adaptive subspace pursuit algorithm, Acta Automatica Sinica, 42, 10, pp. 1512-1519, (2016)
  • [10] Boufounos P.T., Asif M.S., Compressive sampling for streaming signals with sparse frequency content, Proceedings of Annual Conference on Information Science and Systems, pp. 1-6, (2010)
12